Bayesian Network (BN) is a probabilistic graphical model which describes the joint probability distribution over a set of random variables. Finding an optimal network structure based on an available training dataset is one of the most important challenges in the field of BNs. Since the problem of searching the optimal BN structure belongs to the class of NP-hard problems, typically greedy algor...

A Learning Automaton is an automaton that interacts with a random environment, having as its goal the task of learning the optimal action based on its acquired experience. Many learning automata have been proposed, with the class of Estimator Algorithms being among the fastest ones. Thathachar and Sastry [23], through the Pursuit Algorithm, introduced the concept of learning algorithms that pur...

A learning automaton (LA) is an automaton that interacts with a random environment, having as its goal the task of learning the optimal action based on its acquired experience. Many learning automata (LAs) have been proposed, with the class of estimator algorithms being among the fastest ones, Thathachar and Sastry, through the pursuit algorithm, introduced the concept of learning algorithms th...

We consider the problem of a robot manipulator operating in a noisy work space. The robot is assigned the task of moving from P i to P f. Because P i is its initial position, this position can be known fairly accurately. However, because P f is usually obtained as a result of a sensing operation, possibly vision sensing, we assume that P f is noisy. We propose a solution to achieve the motion t...